| [BMP08] | Peter L. Bartlett, Shahar Mendelson, and Petra Philips. Optimal sample-based estimates of the expectation of the empirical minimizer. ESAIM: Probability and Statistics, 2008. (Accepted). [ bib | .ps.gz | .pdf | Abstract ] |
| [Bar08] | Peter L. Bartlett. Fast rates for estimation error and oracle inequalities for model selection. Econometric Theory, 24(2), 2008. (To appear. Was Department of Statistics, U.C. Berkeley Technical Report number 729, 2007). [ bib | .pdf | Abstract ] |
| [BW08] | Peter L. Bartlett and Marten H. Wegkamp. Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 2008. (To appear.). [ bib | .ps.gz | .pdf | Abstract ] |
| [RBR08] | Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein. Shifting: one-inclusion mistake bounds and sample compression. Journal of Computer and System Sciences, 2008. (To appear. Was University of California, Berkeley, EECS Department Technical Report EECS-2007-86). [ bib | .pdf ] |
| [CGK+08] | Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, and Peter L. Bartlett. Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks. Journal of Machine Learning Research, 2008. (Accepted). [ bib | .pdf | Abstract ] |
| [LBW08] | Wee Sun Lee, Peter L. Bartlett, and Robert C. Williamson. Correction to the importance of convexity in learning with squared loss. IEEE Transactions on Information Theory, 2008. (To appear). [ bib | .pdf ] |
| [TB07] | Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8:1007-1025, May 2007. (Invited paper). [ bib | .html ] |
| [BT07a] | Peter L. Bartlett and Ambuj Tewari. Sparseness vs estimating conditional probabilities: Some asymptotic results. Journal of Machine Learning Research, 8:775-790, April 2007. [ bib | .html ] |
| [BT07b] | Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Journal of Machine Learning Research, 8:2347-2368, 2007. [ bib | .pdf | Abstract ] |
| [BJM06b] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473):138-156, 2006. (Was Department of Statistics, U.C. Berkeley Technical Report number 638, 2003). [ bib | .ps.gz | .pdf | Abstract ] |
| [BM06b] | Peter L. Bartlett and Shahar Mendelson. Empirical minimization. Probability Theory and Related Fields, 135(3):311-334, 2006. [ bib | .ps.gz | .pdf | Abstract ] |
| [BJM06a] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Comment. Statistical Science, 21(3):341-346, 2006. [ bib ] |
| [BM06a] | Peter L. Bartlett and Shahar Mendelson. Discussion of “2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization” by V. Koltchinskii. The Annals of Statistics, 34(6):2657-2663, 2006. [ bib ] |
| [BBM05] | Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson. Local Rademacher complexities. Annals of Statistics, 33(4):1497-1537, 2005. [ bib | .ps | .pdf | Abstract ] |
| [BJM04] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Discussion of boosting papers. The Annals of Statistics, 32(1):85-91, 2004. [ bib | .ps.Z | .pdf ] |
| [LCB+04] | G. Lanckriet, N. Cristianini, P. L. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research, 5:27-72, 2004. (http://www.jmlr.org/papers/volume5/lanckriet04a/lanckriet04a.pdf). [ bib | .ps.gz | .pdf ] |
| [GBB04] | E. Greensmith, P. L. Bartlett, and J. Baxter. Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5:1471-1530, 2004. [ bib | .pdf ] |
| [BM03] | Peter L. Bartlett and Wolfgang Maass. Vapnik-Chervonenkis dimension of neural nets. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 1188-1192. MIT Press, 2003. Second Edition. [ bib | .ps.gz | .pdf ] |
| [Bar03] | Peter L. Bartlett. An introduction to reinforcement learning theory: value function methods. In Shahar Mendelson and Alexander J. Smola, editors, Advanced Lectures on Machine Learning, volume 2600, pages 184-202. Springer, 2003. http://link.springer.de/link/service/series/0558/bibs/2600/26000184.htm. [ bib ] |
| [GBSTW02] | Y. Guo, P. L. Bartlett, J. Shawe-Taylor, and R. C. Williamson. Covering numbers for support vector machines. IEEE Transactions on Information Theory, 48(1):239-250, 2002. [ bib ] |
| [BM02] | P. L. Bartlett and S. Mendelson. Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3:463-482, 2002. http://www.jmlr.org/papers/volume3/bartlett02a/bartlett02a.pdf. [ bib | .pdf ] |
| [BBL02] | P. L. Bartlett, S. Boucheron, and G. Lugosi. Model selection and error estimation. Machine Learning, 48:85-113, 2002. http://www.stat.berkeley.edu/~bartlett/papers/bbl-msee.ps.gz. [ bib | .ps.gz ] |
| [BB02] | P. L. Bartlett and J. Baxter. Estimation and approximation bounds for gradient-based reinforcement learning. Journal of Computer and System Sciences, 64(1):133-150, 2002. [ bib ] |
| [BBD02] | P. L. Bartlett and S. Ben-David. Hardness results for neural network approximation problems. Theoretical Computer Science, 284(1):53-66, 2002. (special issue on Eurocolt'99). [ bib | http ] |
| [BFH02] | P. L. Bartlett, P. Fischer, and K.-U. Höffgen. Exploiting random walks for learning. Information and Computation, 176(2):121-135, 2002. http://dx.doi.org/10.1006/inco.2002.3083. [ bib | http ] |
| [MBG02] | L. Mason, P. L. Bartlett, and M. Golea. Generalization error of combined classifiers. Journal of Computer and System Sciences, 65(2):415-438, 2002. http://dx.doi.org/10.1006/jcss.2002.1854. [ bib | http ] |
| [BB01] | J. Baxter and P. L. Bartlett. Infinite-horizon gradient-based policy search. Journal of Artificial Intelligence Research, 15:319-350, 2001. [ bib | .html ] |
| [BBW01] | J. Baxter, P. L. Bartlett, and L. Weaver. Experiments with infinite-horizon, policy-gradient estimation. Journal of Artificial Intelligence Research, 15:351-381, 2001. http://www.cs.washington.edu/research/jair/abstracts/baxter01b.html. [ bib | .html ] |
| [AB00] | M. Anthony and P. L. Bartlett. Function learning from interpolation. Combinatorics, Probability, and Computing, 9:213-225, 2000. [ bib ] |
| [MBBF00] | L. Mason, J. Baxter, P. L. Bartlett, and M. Frean. Functional gradient techniques for combining hypotheses. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 221-246. MIT Press, 2000. [ bib ] |
| [SBSS00] | A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans. Introduction to large margin classifiers. In Advances in Large Margin Classifiers, pages 1-29. MIT Press, 2000. [ bib ] |
| [BBDK00] | P. L. Bartlett, S. Ben-David, and S. R. Kulkarni. Learning changing concepts by exploiting the structure of change. Machine Learning, 41(2):153-174, 2000. [ bib ] |
| [PPB00] | S. Parameswaran, M. F. Parkinson, and P. L. Bartlett. Profiling in the ASP codesign environment. Journal of Systems Architecture, 46(14):1263-1274, 2000. [ bib ] |
| [SSWB00] | B. Schölkopf, A. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12(5):1207-1245, 2000. [ bib ] |
| [KBB00] | L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Direct iterative tuning via spectral analysis. Automatica, 36(9):1301-1307, 2000. [ bib ] |
| [MBB00] | L. Mason, P. L. Bartlett, and J. Baxter. Improved generalization through explicit optimization of margins. Machine Learning, 38(3):243-255, 2000. [ bib ] |
| [BL99] | P. L. Bartlett and G. Lugosi. An inequality for uniform deviations of sample averages from their means. Statistics and Probability Letters, 44(1):55-62, 1999. [ bib ] |
| [Bar99] | P. L. Bartlett. Efficient neural network learning. In V. D. Blondel, E. D. Sontag, M. Vidyasagar, and J. C. Willems, editors, Open Problems in Mathematical Systems Theory and Control, pages 35-38. Springer Verlag, 1999. [ bib ] |
| [BST99] | P. L. Bartlett and J. Shawe-Taylor. Generalization performance of support vector machines and other pattern classifiers. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 43-54. MIT Press, 1999. [ bib ] |
| [SFBL98] | R. E. Schapire, Y. Freund, P. L. Bartlett, and W. S. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5):1651-1686, 1998. [ bib ] |
| [BMM98] | P. L. Bartlett, V. Maiorov, and R. Meir. Almost linear VC dimension bounds for piecewise polynomial networks. Neural Computation, 10(8):2159-2173, 1998. [ bib ] |
| [LBW98] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. The importance of convexity in learning with squared loss. IEEE Transactions on Information Theory, 44(5):1974-1980, 1998. [ bib ] |
| [STBWA98] | J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5):1926-1940, 1998. [ bib ] |
| [BLL98] | P. L. Bartlett, T. Linder, and G. Lugosi. The minimax distortion redundancy in empirical quantizer design. IEEE Transactions on Information Theory, 44(5):1802-1813, 1998. [ bib ] |
| [BK98] | P. L. Bartlett and S. Kulkarni. The complexity of model classes, and smoothing noisy data. Systems and Control Letters, 34(3):133-140, 1998. [ bib ] |
| [BV98] | P. L. Bartlett and M. Vidyasagar. Introduction to the special issue on learning theory. Systems and Control Letters, 34:113-114, 1998. [ bib ] |
| [KBB98] | L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Optimal controller properties from closed-loop experiments. Automatica, 34(1):83-91, 1998. [ bib ] |
| [BL98] | P. L. Bartlett and P. M. Long. Prediction, learning, uniform convergence, and scale-sensitive dimensions. Journal of Computer and System Sciences, 56(2):174-190, 1998. (special issue on COLT`95). [ bib ] |
| [Bar98] | P. L. Bartlett. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2):525-536, 1998. [ bib ] |
| [BKP97] | P. L. Bartlett, S. R. Kulkarni, and S. E. Posner. Covering numbers for real-valued function classes. IEEE Transactions on Information Theory, 43(5):1721-1724, 1997. [ bib ] |
| [Bar97] | P. L. Bartlett. Book review: `Neural networks for pattern recognition,' Christopher M. Bishop. Statistics in Medicine, 16(20):2385-2386, 1997. [ bib ] |
| [LBW97] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. Correction to `lower bounds on the VC-dimension of smoothly parametrized function classes'. Neural Computation, 9:765-769, 1997. [ bib ] |
| [LBW96] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. Efficient agnostic learning of neural networks with bounded fan-in. IEEE Transactions on Information Theory, 42(6):2118-2132, 1996. [ bib ] |
| [ABIST96] | M. Anthony, P. L. Bartlett, Y. Ishai, and J. Shawe-Taylor. Valid generalisation from approximate interpolation. Combinatorics, Probability, and Computing, 5:191-214, 1996. [ bib ] |
| [BLW96] | P. L. Bartlett, P. M. Long, and R. C. Williamson. Fat-shattering and the learnability of real-valued functions. Journal of Computer and System Sciences, 52(3):434-452, 1996. (special issue on COLT`94). [ bib ] |
| [BW96] | P. L. Bartlett and R. C. Williamson. The Vapnik-Chervonenkis dimension and pseudodimension of two-layer neural networks with discrete inputs. Neural Computation, 8:653-656, 1996. [ bib ] |
| [LBW95] | W. S. Lee, P. L. Bartlett, and R. C. Williamson. Lower bounds on the VC-dimension of smoothly parametrized function classes. Neural Computation, 7:990-1002, 1995. (See also correction, Neural Computation, 9: 765-769, 1997). [ bib ] |
| [Bar94] | P. L. Bartlett. Computational learning theory. In A. Kent and J. G. Williams, editors, Encyclopedia of Computer Science and Technology, volume 31, pages 83-99. Marcel Dekker, 1994. [ bib ] |
| [Bar93] | P. L. Bartlett. Vapnik-Chervonenkis dimension bounds for two- and three-layer networks. Neural Computation, 5(3):371-373, 1993. [ bib ] |
| [LBD92] | D. R. Lovell, P. L. Bartlett, and T. Downs. Error and variance bounds on sigmoidal neurons with weight and input errors. Electronics Letters, 28(8):760-762, 1992. [ bib ] |
| [BD92] | P. L. Bartlett and T. Downs. Using random weights to train multi-layer networks of hard-limiting units. IEEE Transactions on Neural Networks, 3(2):202-210, 1992. [ bib ] |
This file was generated by bibtex2html 1.91.